IMPLEMENTASI INTELLIGENCE TRAFFIC SYSTEM MENGGUNAKAN NEURO FUZZY DAN SELF-ORGANIZING MAP (SOM) DI KOTA SURABAYA
| Main Authors: | I Gede , Susrama, Slamet, Winardi, Mohamad Irwan, Afandi |
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| Format: | Proceeding PeerReviewed application/pdf |
| Terbitan: |
, 2009
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| Subjects: | |
| Online Access: |
http://eprints.upnjatim.ac.id/1365/1/I_Gede_Susrama_Mas_Diyasa_1)%2C_Slamet_Winardi_2)%2C_Mohamad_Irwan_Afandi_3)_60%2D66.pdf http://www.upnjatim.ac.id http://eprints.upnjatim.ac.id/1365/ |
Daftar Isi:
- At this time, traffic congestion has been often found in big cities in Indonesia, including the city of Surabaya, especially at rush hour. One indicator of traffic congestion is the speed of travel or time travel on segments of the city road network. By looking at the correlation of traffic volume, it can be seen that the level of road services is a fundamental need for information system development step road network. This study aimed to identify the importance of travel time information for modification is the vehicle travel speed and vice versa. From this study are expected measurement measures the travel time and speed the average travel roads can be understood so that the correlation relationship between the speed of traffic can be controlled by the Traffic Intelligence system. Speed is the level of traffic movement or a particular vehicle is often expressed in kilometres/hour. There are two categories of average velocity. The first is the speed of the average time that is the average of the number of speed at certain locations. The second is the speed or the average speed of travel which includes travel and time constraints. Velocity space average is calculated based on travel distance divided by time travel on certain roads. This speed can be determined through the measurement of travel time and obstacles. In this study proposed a motion detection estimation algorithm using Self Organizing Maps (SOM) models perform detection and normalization, segmentation matching block (Block Matching Algorithm) with the correlation hierarchy search method phase (Phase Correlation hierarchical search method) or a search technique called correspondence Coarse -to-fine (Coarse-to-fine correspondence search technique). This technique takes a long time in computing, but very good detection results. To overcome the long computing time, the neuro fuzzy method is used as a decision-making process in determining Artificial Intelligence. Keywords : Intelligent Traffic System, SOM, Neuro Fuzzy